The positioning of a moving platform, such as, for example, vehicles or individuals, is commonly achieved using known reference-based systems which are absolute navigation systems, such as, among others, the Global Navigation Satellite Systems (GNSS). The GNSS comprises a group of satellites that transmit encoded signals and receivers on the ground, by means of trilateration techniques, can calculate their position using, for example, the travel time of the satellites' signals and information about the satellites' current location.
Currently, the most popular form of GNSS for obtaining absolute position measurements is the global positioning system (GPS), which is capable of providing accurate position and velocity information provided that there is sufficient satellite coverage. However, in any GNSS, where the satellite signal becomes disrupted or blocked such as, for example, in urban settings, tunnels, canopies, dense foliages, mines, and other GNSS-degraded or GNSS-denied environments, a degradation or interruption or “gap” in the GPS positioning information can result.
In order to achieve more accurate, consistent and uninterrupted positioning information, GNSS information may be augmented with additional positioning information obtained from complementary positioning systems. Such systems may be self-contained and/or “non-reference based” systems within the platform, and thus need not depend upon external sources of information that can become interrupted or blocked.
One such “non-reference based” or relative positioning system is the inertial navigation system (INS). Inertial sensors are self-contained sensors within the platform that use gyroscopes to measure the platform's rate of rotation/angle, and accelerometers to measure the platform's specific force (from which acceleration is obtained). Using initial estimates of position, velocity and orientation angles of the moving platform as a starting point, the INS readings can subsequently be integrated over time and used to determine the current position, velocity and orientation angles of the platform. Typically, measurements are integrated once for gyroscopes to yield orientation angles and twice for accelerometers to yield position of the platform incorporating the orientation angles. Thus, the measurements of gyroscopes will undergo a triple integration operation during the process of yielding position. Inertial sensors alone, however, are unsuitable for accurate positioning because the required integration operations of data results in positioning solutions that drift with time, thereby leading to an unbounded accumulation of errors.
Given that each positioning technique described above may suffer either loss of information or errors in data, common practice involves integrating the information/data obtained from the GNSS with that of the INS. For instance, to achieve a better positioning solution, INS and GPS data may be integrated because they have complementary characteristics. INS readings are accurate in the short-term, but their errors increase without bounds in the long-term due to inherent sensor errors. GNSS readings are not as accurate as INS in the short-term, but GNSS accuracy does not decrease with time, thereby providing long-term accuracy. Also, GNSS may suffer from outages due to signal blockage, multipath effects, interference or jamming, while INS is immune to these effects.
Although available, integrated INS/GNSS is not often used commercially for low cost applications because of the relatively high cost of navigational or tactical grades of inertial measurement units (IMUs) needed to obtain reliable independent positioning and navigation during GNSS outages. Low cost, small, lightweight and low power consumption Micro-Electro-Mechanical Systems (MEMS)-based inertial sensors may be used together with low cost GNSS receivers, but the performance of the navigation system will degrade very quickly in contrast to the higher grade IMUs in areas with little or no GNSS signal availability due to time-dependent accumulation of errors from the INS.
The integration of INS and GNSS rely on a filtering technique or a state estimation technique such as, for example, Kalman filter (KF), Linearalized KF (LKF), Extended KF (EKF), Unscented KF (UKF), and Particle filter (PF) among others.
The KF, as an example, estimates the system state at some time point and then obtains observation “updates” in the form of noisy measurements. As such, the equations for the KF fall into two groups:                Time update or “prediction” equations: used to project forward in time the current state and error covariance estimates to obtain an a priori estimate for the next step, or        Measurement update or “correction” equations: used to incorporate a new measurement into the a priori estimate to obtain an improved posteriori estimate.        
There are several ranging systems that can be used to measure distances between transmitters and receivers. Examples of such systems are WiFi™, Bluetooth™, ZigBee™, Radio Frequency ID Tags (RFID), Ultra-Wideband (UWB), and dedicated radio frequency (RF) transceivers, such as 457 kHz avalanche transceivers (e.g. Mammut Pulse Barryvox™).
If a vehicle, equipped with a ranging system and an integrated INS/GNSS navigation system, operates in areas with little or no GNSS signal availability, the navigation accuracy will degrade with time due to time-dependent accumulation of errors from the INS. The ranging system of the vehicle is commonly used to detect road hazards such as other vehicles without providing any aid to the navigation solution.
Commercially available systems using wireless signals for positioning purposes, in order to derive a position of a roving receiver with respect to a base station, typically use a method, such as for example: proximity location, trilateration or fingerprinting. Proximity location sets the position of the remote receiver at the position of the known base station making it a rough approximation method. Trilateration requires multiple ranging signals and is best employed in scenarios with a dense grid of base stations with overlapping ranges. Fingerprinting requires access to a pre-survey database with known base stations positions. The main pitfall with fingerprinting is maintenance of an accurate database, which cannot be enabled when the actual base stations are moving, as is the case of moving base station platforms. Fingerprinting is more useful when the base stations are fixed in location, such as WiFi access points (AP's). Other wireless methods use angle of arrival (e.g. Radar) to determine a more accurate location. These systems require installation of special directional or multi-element antennas.
All of these wireless positioning techniques need more than one wireless piece of information in order to provide accurate rover position, especially when the base station is moving. When only a single wireless range measurement is available, it is not enough to provide acceptable positioning accuracy.